The methodologies of SPET and PET are described in detail in several standard textbooks on Nuclear Medicine. Broadly speaking, the methodologies involve constructing a three-dimensional map of radioactivity sources within an entity or target body and displaying relative levels of radioactivity emanating from each of a plurality of volume elements making up the entity. The radioactivity sources are generally gamma radiation emitters (radiotracers), which have been injected into the body of a patient or other target body, and these are detected by a gamma camera which images a plurality of views of the body as illustrated in FIGS. 1 and 2. Three-dimensional mapping of other organic or non-organic entities is also achieved with this technique.
The gamma camera typically comprises a detector 10 and a collimator 15 adapted to image emissions parallel to the axis of the collimator, i.e., as depicted in FIG. 1(a), in the x direction. The camera rotates incrementally about an axis of rotation R, which is preferably coincident with the center of the target body 20; and successive images are captured at varying angles of theta degrees, for example, as shown at FIG. 1(b) where θ=45° and at FIG. 1(c) in the −y direction where θ=90°. Typically, images are taken every 2°–6°. The resulting set of images, also referred to as data frames, records total radiation counts for, or projections of, a plurality of parallel columns 22 passing through the target body 20. These images are then back-projected using known techniques to compute the relative number of counts originating from each pixel (i, j) of a matrix 35 shown in FIG. 1(d) representing a transaxial or transverse slice through the target body 20. Each pixel (i, j) has a finite thickness in the z direction along the rotational axis R, which is a function of the equipment and acquisition parameters being used, and thus corresponds to a volume element or “voxel” (i, j, k) occupying the transaxial or transverse (x-y) plane. Additional transaxial slices are imaged at succeeding positions along the z axis, and from this further information, relative numbers of radiation counts from all voxels in the target body 20 are calculated to reconstruct a three-dimensional “image” of radioactivity within the target body 20.
Such back-projection techniques are well known in the art but make a substantial number of approximations and assumptions about the collected data, which result in an effective filtering of the data when reconstructing the voxel data. In particular, back-projection techniques use an averaging process to determine the values of individual voxels, and thus introduce a smoothing effect, i.e. a high-frequency filtration of the data. Thus, the voxel map produced in the reconstructed image cannot be considered as “raw data”, and further processing (e.g., for image enhancement or quantitative analysis) of the reconstructed data can encounter problems with unwanted filtering artifacts such as aliasing and the like.
For reconstructing an image, a digital data processing system back-projects to a predetermined back-projection matrix 35 or grid, such as that shown in FIG. 1(d). Because of this, a substantial amount of interpolation of the projected data is required when a projection such as that shown in FIG. 1(b) is not aligned with the back-projection matrix FIG. 1(d). In other words, the quantized x′ axis of the camera must be mapped to the quantized x-y axes of the matrix 35. Various schemes exist for such interpolation, varying from “nearest-pixel” mapping to linear and even more complex interpolation methods, all of which introduce a further element of data filtration. Filtering is also required, subsequent to this interpolation in particular, to remove a so-called “star” artifact, which also introduces interdependence among voxel values that prevents derivation of accurate quantitative values.
Attenuation and scatter corrections also contribute to data filtering. Each projection image provides a total radiation count for a given column 22. A simple, but unsophisticated technique for back-projection is to assume, in the first instance, that the radiation count derives from sources distributed evenly throughout the length of the column, before weighting the projection data with counts from other images transverse thereto in the reconstruction process. However, this simplistic approach ignores known attenuation and scatter factors for radiation passing through the body 20, for which approximate corrections can be made during the back-projection process. Although resulting in a more accurate final image, such corrections also introduce filtering artifacts, which can further contaminate later data processing.
High intensity objects and sharp edges can also introduce a considerable amount of blur, including obscuring artifacts, into in the reconstructed image. Frequency domain highpass filters are often used to remove the blur, but there is no perfect way to remove this blur and associated obscuring artifacts within the images. If the intensities of some of the objects are much higher than others, then there will be more blur and hence more artifacts. If a high intensity object is located alongside a low intensity object, then the reconstruction artifacts could completely hide or distort the low intensity object in the reconstructed image.
All of the above factors can severely limit the ability to accurately quantify the reconstructed data derived from SPET and PET studies. In addition, the accuracy of quantitative data can be affected by other factors such as noise, sampling errors, detector characteristics, time variance of the radioactivity distribution during the period of acquisition, and the type of reconstruction algorithm and filtering used. Much work has been done towards estimating the effects of each of these factors and towards providing methods attempting to deal with the inaccuracies caused, such as those described in European Journal of Nuclear Medicine 19(1), 1992, pp. 47–61; K A Blokland et al: “Quantitative analysis in single photon emission tomography.” However, none of the approaches are in widespread use and they fail to tackle a fundamental problem, namely, that of maintaining statistical independence of the sets of pixels or voxels of the reconstructed images (i.e., the projection data).
The quantitative measurement of radiation sourced from a region of interest within a body is a highly desirable goal in a number of fields. In particular, there are many clinical benefits to obtaining the quantitative measurements, such as enabling a clinician to more accurately locate and analyze a disease site in a scanned patient. The use of time as an additional factor in deriving quantitative data further enhances the ability to make dosimetry measurements for radionuclide therapy of cancer and other diseases. However, it will be understood that such techniques have a far wider applicability beyond aiding diagnosis and therapy of the living body.
There are two well-known reconstruction (inverse radon transform) techniques: an analytical solution based on Fourier transforms and an algebraic solution, such as ART (algebraic reconstruction technique) or the back projection method. A third technique, referred to as geometric tomography, processes raw projection data using a geometry-based approach to reconstruct the boundaries of objects. The technique provides a way to define geometrical surfaces, including their changes over time, but does not replace the digital images produced by conventional computed tomography for evaluating radiological distributions within the target body. A detailed description of geometric tomography can be found in a paper by Jean-Philippe Thirion, entitled Segmentation of Tomographic Data Without Image Reconstruction, published in IIEEE Transactions on Medical Imaging, Vol. 11, No. 1, March 1992 and in U.S. Pat. No. 5,421,330 to Thirion et al., which is hereby incorporated by reference.
An International Patent Application Publication WO 97/05574, entitled Raw Data Segmentation and Analysis in Image Tomography, describes the processing of raw tomographic data to isolate selected portions of the data, particularly objects defined by their radiological concentrations, for analyzing selected objects of interest or for removing the radiological contributions of selected objects that obscure the imaging of other portions of the data having more interest. The corresponding International Application No. PCT/GB96/01814 is also incorporated by reference.
The suggested raw data segmentation can be used to remove one or more high intensity objects of little or no interest from the raw data so that the reconstructed images will provide a much clearer picture of the remaining objects. Artifacts are reduced while lighter intensity objects, which would not be visually apparent among the higher intensity objects, become visibly apparent in the reconstructed image.
Similar to geometric tomography, object boundaries are found by evaluating projection data arranged in a sequence of sinograms in which data collected for each transaxial slice is arranged in an array having a first axis along the intersection of the transaxial slice and the plane of the gamma camera (normal to the transaxial slice) and a second axis along the gamma camera's angle of acquisition. Individual objects appear as sinusoidal traces in the sinograms, each having an amplitude corresponding to the object's average distance from the rotational axis R and a thickness corresponding to the apparent width of the object within the transaxial slice at the considered angle of acquisition.
Different objects trace different sinusoidal traces that can overlap each other within the sinograms. Distinguishing one object from another within the sinograms involves considerable manual and computer processing. Typically, angular sections within each sinogram along which the object traces appear in isolation from other object traces are manually identified and various edge-finding and curve-fitting techniques provide approximations for completing the individual traces. However, in addition to the spatial boundaries of objects within sinograms, the radiological counts, which define the intensities of the objects, also overlap; and approximations are required to divvy the counts between the overlapping traces of the objects. The approximations are generally based on an interpolation between the number counts emitted by the object immediately before and immediately after the intersection of its trace with the trace of another object. Each sinogram within which the selected object appears must be separately evaluated out of the total number of transaxial slices at which projection data is collected. Thus, the segmentation of data is difficult, time consuming, and itself subject to variation depending on the accuracy with which the isolated sections of the object traces are identified and the rules for approximating of the remaining object data.